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Abstract
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Normal gait is dependent on the proper functioning of the peripheral, musculoskeletal, and psychological body systems as well as of the neural network of the brain. Gait function can serve as a decisive factor for evaluating health conditions in clinical settings.1
Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the central nervous system, with gait impairment being one of its characteristic symptoms. Falls mostly occur in the advanced stages of PD, but they can also appear in the early stages. Extensive research is underway into how to predict falls from the early stages of PD, since their occurrence have a significant impact on the quality of life, socioeconomic burden, and mortality rates. However, assessing the fall risk is challenging due to the complex interplay of both disease-specific and generic factors. Moreover, falls and freezing of gait in PD can be influenced by psychological factors that render these events episodic and highly variable.
Previous research aimed at predicting the risk of falls has employed strategies that go beyond simple gait analysis, using multiple tasks while walking to increase the sensitivity and yield. However, the results when using such complex tasks are difficult to interpret accurately since the outcomes are influenced by factors other than gait itself, which restricts the effectiveness of these approaches. Indeed, a recent study that identified a direct association between fall scores and visual hallucinations in PD highlighted the intricate interplay of various factors in fall prediction.2
Technology-based objective measurements (TOMs) have recently been adopted to visualize, quantify, and temporally record subtle changes of the body movements. TOMs are increasingly employed in the field of neurology, especially in movement disorders, including in gait analysis.3 This technology has made gait analysis more objective and precise, allowing for the widespread adoption of temporal analyses and diagnostic processes. This approach facilitates the analysis of baseline clinical parameters that in turn enables its use in various risk-prediction applications.
In this context, the study by Kwon et al.4 reported in the current issue has demonstrated the relationship between baseline gait parameters from quantitative analysis and falls in individuals with drug-naïve PD, and also identified key gait parameters for predicting falls. A major strength of that study was the analysis of gait patterns during backward walking, a behavior not commonly performed in routine gait assessments, which could be helpful in revealing subtle abnormalities and facilitate sensitive risk predictions.
Language: en |